Multi-trajectory pose correspondences using scale-dependent topological analysis of pose-graphs

This paper considers the problem of finding pose matches between trajectories of multiple robots in their respective coordinate frames or equivalent matches between trajectories obtained during different sessions. Pose correspondences between trajectories are mediated by common landmarks represented in a topological map lacking distinct metric coordinates. Despite such lack of explicit metric level associations, we mine preliminary pose level correspondences between trajectories through a novel multi-scale heat-kernel descriptor and correspondence graph framework. These serve as an improved initialization for ICP (Iterative Closest Point) to yield dense pose correspondences. We perform extensive analysis of the proposed method under varying levels of pose and landmark noise and showcase its superiority in obtaining pose matches in comparison with standard ICP like methods. To the best of our knowledge, this is the first work of the kind that brings in elements from spectral graph theory to solve the problem of pose correspondences in a multi-robotic setting and differentiates itself from other works.